Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell.

ABSTRACT: Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the inherently unstable and unreliable energy harvesters. In this work, we fabricated a robust binarized neural network comprising 32,768 memristors, powered by a miniature wide-bandgap solar cell optimized for edge applications. Our circuit employs a resilient digital near-memory computing approach, featuring complementarily programmed memristors and logic-in-sense-amplifier. This design eliminates the need for compensation or calibration,

SUBMITTER: Jebali F 

PROVIDER: S-EPMC10811339 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature